Deploying machine learning models from training to production requires companies to deal with the complexity of moving workloads through different pipelines and re-writing code from scratch.

Or Zilberman will demonstrate how simple it is to automatically transfer a full machine learning pipeline from Jupyter notebook to scale-out serverless functions for event-driven and real-time applications.

He will also address versioning challenges, showing how serverless functions can enable developers to update machine learning models and code together as a single versioned entity. The session will include a deep walkthrough and interactive demos.